Correcting 'bad' translations in a sequence-to-sequence neural machine translation model
The basic process you're describing sounds a lot like Boosting, which is also well covered in Chapter 18 of Russell &amp; Norvig, or like active learning.

You're correct that training the model with emphasis on a specific subset of the data is likely to lead to mistakes somewhere else. Boosting gets around this in a clever way.


It begins by training a model as normal.
The new model's performance on the training set is measured.
A new dataset is generated, where examples that were misclassified by the previous model are more highly weighted (this is intuitively similar to adding an extra copy of them to the dataset).
A new model is trained on the weighted dataset. The model suffers a greater penalty for making the same kinds of errors as the previous model(s), and so tends to make different errors.
Steps 2-4 are repeated for some time.
An ensemble of models is output, rather than a single model.


To classify new points, the most common score from all ensemble members is used.

I'm less familiar with NLP work using this approach, but it seems like the same basic idea could be used fruitfully here: the notion is to train many models that make independent errors, and then trust their collective decisions.